RÉSUMÉ
Our reviews intend to provide a comprehensive update for the diagnosis of ovarian cancer using biomarkers (CA125) and ultrasonography algorithm RMI and ROMA and how to improve our approaches to identify and diagnose ovarian cancer in early stage and improve the survival rate This meta-analysis done in the Department of Obstetrics and Gynaecology Swaroop Rani hospital Prayagraj, Uttar Pradesh. Source of literature was all the standard online articles available in last 3years and also the departmental records of the last 1year following criteria CA125, HE4, RMI, ROMA for screening or diagnosis. Our result on the basis of ODDS ratio and confidence interval (CI) of tumour marker like CA125, HE4, RMI, ROMA from different study. Overall estimation and pooled estimation determined by forest plot and our pooled estimation is present between the 0.8 OR to 1.0 OR and 95 % of confidence interval of the result. Our study concludes that RMI has comparatively good OR ratio and pooled effect of forest plot is in favour of RMI 0.8 OR to 1.0 OR and had good opportunity to identify early-stage ovarian cancer. Our study concludes that RMI score has comparatively good OR ratio and it can be used to identify early-stage ovarian cancer which can help in on time intervention and improved outcomes in such patients.
RÉSUMÉ
OBJETIVO: evaluar la experiencia en la utilización del método GIRADS para clasificar masas anexiales a diez años de su primera publicación. MÉTODO: Se realizó búsqueda de estudios que utilizan el sistema GIRADS: Medline (Pubmed), Google Scholar y Web of Science, desde enero de 2009 hasta diciembre de 2019. Se calculó la sensibilidad y especificidad agrupada, Likelihood ratio (LR) (+) y LR (-) y Odds ratio de diagnóstico (DOR). La calidad de los estudios se evaluó con QUADAS-2. RESULTADOS: Se identificaron 15 estudios y se incluyeron 13 de ellos con 4473 masas, 878 de ellas malignas. La prevalencia media de malignidad ovárica fue del 23 % y la agrupada de 19.6%. El riesgo de sesgo fue alto en cuatro estudios para el dominio "selección de pacientes" y fue bajo en todos en todos los estudios para los dominios "prueba índice" y "prueba de referencia". La sensibilidad, especificidad, LR (+) y LR (-) agrupadas y el DOR del sistema GIRADS para clasificar las masas anexiales fueron: 96.8% (intervalo de confianza [IC] 95% = 94% - 98%), 91.2 % (IC 95 % = 85% - 94%), 11.0 (IC 95% = 6.9 -13.4) y 0.035 (IC 95% = 0.02- 0.09), y 209 (IC 95% = 99-444), respectivamente. La heterogeneidad fue alta para la sensibilidad y especificidad. De acuerdo a la metaregresión, la heterogeneidad entre los estudios se explica por la prevalencia de malignidad, múltiples observadores y la ausencia de diagnóstico histopatológico para todos los casos incluidos en un determinado estudio. CONCLUSIÓN: el sistema GIRADS tiene un buen rendimiento diagnóstico para clasificar masas anexiales.
OBJECTIVE: to evaluate the experience of using GIRADS method to classify adnexal masses ten years after its publication. METHOD: A search was carried out for studies reporting on the use of the GIRADS system in the Medline (Pubmed), Google Scholar and Web of Science databases, from January 2009 to December 2019. Pooled sensitivity and specificity, Likelihood ratio (LR) (+) and LR (-) and Diagnostic Odds ratio (DOR) were calculated. The quality of the studies was assessed by QUADAS-2. RESULTS: 15 studies were identified, and 13 of them were included with 4473 masses, of which 878 were malignant. The mean prevalence of ovarian malignancy was 23% and the prevalence pooled. of 19.6%. The risk of bias was high in four studies for the domain 'patient selection' and low for all studies for the domains 'index test' and 'reference test'. The sensitivity, specificity, pooled LR (+) and LR (-) and the DOR of the GIRADS system to classify adnexal masses were 96.8% (95% confidence interval [CI] = 94% -98%), 91.2 % (95% CI = 85% -94%), 11.0 (95% CI = 6.9-13.4) and 0.035 (95% CI = 0.02-0.09), and 209 (95% CI = 99-444), respectively. Heterogeneity was high for both sensitivity and specificity. According to meta-regression, this heterogeneity was explained by the prevalence of malignancy, the use of multiple observers, and the absence of histopathological diagnosis for all cases included in a given study. CONCLUSION: the GIRADS system has a good diagnostic performance to classify adnexal masses.